• WSJ contains collocations which are closer to those of press:reportage, than those from the rest of the BC.

having related genre/topics help having common collocations, and therefore, warrant better word sense disambiguation performance.


SNoW has already been used successfully on several tasks in natural language processing (Roth, 1998;Roth and Zelenko, 1998;Golding and Roth, 1999;Punyakanok and Roth, 2001).

for each class label SNoW learns a function' © ¥ u !


At prediction time, given Q , SNoW outputs All functions -in our case, D 9 ¥ target nodes are used, one for each pos tag -reside over the same feature space, but can be thought of as autonomous functions (networks).

a given example is treated autonomously by each target subnetwork; an example labeled is considered as a positive example for the function learned for and as a negative example for the rest of the functions (target nodes).


Although SNoW is used with D 9 ¥ different targets, the SM utilizes by determining the confusion set dynamically.

in evaluation (prediction), the maximum in Eq.


Moreover, in training, a given example is used to train only target networks that are in the currently applicable confusion set.

an example that is positive for target , is viewed as positive for this target (if it is in the confusion set), and as negative for the other targets in the confusion set.


During evaluation, whenever word P occurs, it is tagged with one of these pos tags.

in evaluation, the confusion set consists only of those tags observed with the target word in training, and the maximum in Eq.


On the other hand, the SM classifier is trained on a -tagged example of word , by using it as a positive example for and a negative example only for the effective confusion set.

those pos tags which have been observed as tags of in the training corpus.


In this section, we discuss some of the theoretical aspects of the SM and explain some of its advantages.


By conditional independence we mean that , and Ý the hypothesis produced by × .

then, for all Ý Û Ç % Ò Ý , In the limit, as this holds if Ý is a hypothesis produced by × when trained on E , that is sampled according to ë à e ( â 8 â .


Based on this assumption, Claim 2 shows that training this way is advantageous.

that utilizing the SM in training yields a better classifier.


In all the experiments, stratified 10-fold cross-validation was used.

ling-Spam was partitioned into 10 equally populated parts, maintaining the original spam-legitimate ratio.


The results presented here motivate further work in the same direction.

we are interested in combining more classifiers, such as decision trees (Quinlan, 1993) and support vector machines (Drucker, et al.


The numbers in the MeSH descriptors are categorical values; we represented them with indicator variables.

for each variable we calculated the number of possible categories c and then represented an observation of the variable as a sequence of c binary variables in which one binary variable was one and the remaining c − 1 binary variables were zero.


Other test set words such as immunisation and carcinogen do not appear in the training set at all.

it seems that the MeSHk-based categorization does better when generalization is required.


A multiword unit (MWU) is a connected collocation: a sequence of neighboring words "whose exact and unambiguous meaning or connotation cannot be derived from the meaning or connotation of its components" (Choueka, 1988).

mWUs are typically non-compositional at some linguistic level.


we use a randomly-selected corpus the first five columns as "information-like."


Their "canonical-contextual" algorithm, which shares elements with our hybrid method, was the most successful one.

acrophile uses a fixed 40-word search space for their "contextual" definition search and has a set of syntactic cues similar to ours for defining the "canonical" environments in which abbreviation-definition pairs may be found.


Adding training data from from an unmatched corpus doesn't hurt, but doesn't help a great deal either.

lexical bigram statistics appear to be corpus-speci c, and our results show that they  Table 4: Parsing results with pruned probability models.


Unfortunately, as we will describe, the standard algorithm for learning decision lists has an important flaw: it often chooses a rule order that is suboptimal in important ways.

sometimes the algorithm will use a rule that appears good -has lower average entropy -in place of one that is good -lowers the expected entropy given its location in the list.


Therefore, we smooth our estimates (Chen and Goodman, 1999).

we used the interpolated absolute discounting method.


Let N (q i ) be the number of non-zero ys for a given question.

in the two class case, N (q i ) will be 0 if there were no occurences of the question q i , 1 if training samples for q i always had the same value, and 2 if both 1 and 0 values occurred.


First, we note that non-probabilistic decision lists and transformation-based learning (TBL) are actually very similar formalisms.

as observed by Roth (1998), in the two-class case, they are identical.


First, it shows the connection between our work and previous work.

our new algorithm can be thought of as a probabilistic version of the Ramshaw and Marcus (1994) algorithm, for speeding up TBLs.


Their technique does have the advantage that it correctly handles the multi-class case.

by using a decision tree, it is relatively easy to incorporate the current state, while the decision list learner ignores that state.


We thus chose one problem of this type, grammar checking, and believe that our results should carry over at least to these other, closely related problems.

we chose to use exactly the same training, test, problems, and feature sets used by Banko and Brill (2001a;2001b).


The first three were variations on the standard probabilistic decision list learner.

first we ran the standard sorted decision list learner, equivalent to the algorithm of Figure 3, with a threshold of negative infinity.


In particular, first we ran the standard sorted decision list learner, equivalent to the algorithm of Figure 3, with a threshold of negative infinity.

we included all rules that had a predicted entropy at least as good as the unigram distribution, whether or not they would actually improve entropy on the training data.


We present empirical results showing the relative contribution of the component knowledge sources and the different learning algorithms.

using all of these knowledge sources and SVM (i.e., a single learning algorithm) achieves accuracy higher than the best official scores on both SENSEVAL-2 and SENSEVAL-1 test data.


In the results reported in this paper, we consider all words in the provided context.

all tokens in the surrounding context of We attempted a simple feature selection method to investigate if a learning algorithm performs better with or without feature selection.


In this paper, our reported results are based on the official fine-grained scoring method.

to compute an average recall figure over a set of words, we can either adopt micro-averaging (mi) or macro-averaging ma  micro-averaging treats each test instance equally, so that a word task with many test instances will dominate the micro-averaged recall.


The resulting system achieves an Fmeasure of 69.5 and 63.4 on the MUC-6 and MUC-7 coreference resolution data sets, respectively, surpassing the performance of the best MUC-6 and MUC-7 coreference systems.

the system outperforms the best-performing learning-based coreference system to date.


3 Three intrinsic properties of coreference 4 , however, make the formulation of the problem as a classification-based single-link clustering task potentially undesirable: Coreference is a rare relation.

most NP pairs in a document are not coreferent.


Overall, the use of pruning boosts system performance from an F-measure of 69.3 to 69.5, and from 57.2 to 63.4 for the MUC-6 and MUC-7 data sets, respectively, enabling the system to achieve performance that surpasses that of the best MUC coreference systems by 4.6% and 1.6%.

the system outperforms the best-performing learningbased coreference system (Soon et al., 2001) by 6.9% and 3.0%.


We use RIPPER (Cohen, 1995), an information gain-based propositional rule learning system, to train a classifier that, given a test instance , decides whether or not NP § £ and NP¨£ are coreferent.

rIPPEr sequentially covers the positive training instances and induces a ruleset that determines when two NPs are coreferent.


Nevertheless, reducing the number of negative instances (via negative sample selection) improves recall but damages precision: we see statistically significant gains in recall and statistically significant drops in precision for both data sets.

precision drops precipitously from 78.0 to 55.1 for the MUC-7 data set.


14 In comparison to the Combined results, we see an improvement of 0.2% (for MUC-6) and 6.2% (for MUC-7) in F-measure.

we see statistically significant gains in precision (from 55.1 to 73.6) and statistically significant, but much smaller, drops in recall (from 59.5 to 54.2) for the MUC-7 data set.


Let us consider the set R of 2,809,545 possible flat rules that stand at edit distance 1 from the set of S → • • • rules observed in our English training data.

a rule such as S put → NP put NP is in R if it did not appear in training data itself, but could be derived by a single edit from some rule that did appear.


The difference is statistically significant (p < 0.001, bootstrap test).

the bigram model underpredicts the edit-distance "neighbors" of observed rules by a factor of 3.


This transformation model is really a PCFG with unusual parameterization.

for any value of θ, it defines a language-specific probability distribution over all possible context-free rules (graph vertices).


Note that the vertex labels, graph topology, and arc parameters are language independent.

figure 1 is supposed to represent Universal Grammar: it tells a learner what kinds of generalizations to look for.


Figure 2 shows that averaging the transformation model with the Treebank/bigram model improves the latter not merely on balance, but across the board.

there is no evident class of phenomena for which incorporating transformations would be a bad idea.


• Transformations also helped on test rules that had been observed once in training with relatively infrequent words.

(the transformation model does not discount singletons too much.)


Each model was then asked: Does f 1 go with w 1 and f 2 with w 2 , or vice-versa?

which is bigger, p(f Since the frames were novel, the model had to make the choice according to whether f 1 or f 2 looked more like the frames that had actually been observed with w 1 in the past, and likewise w 2 .


Further, it should normalize for text length.

the score I use is: log Pr(two-word links in best matching) log Pr(all links in best matching) This score is an example of Lin's (1998) mathematical definition of similarity, which is motivated by information theory: where X and Y are any objects generated by a probabilistic model.


's r, however, tsim is token-based, incorporating word frequency.

the intersection of two bags (rather than sets) of tokens contains the minimum count (over the intersected bags) of each type; the union contains the maximum counts, e.g., With the assumption of equiprobability, any translation lexicon (or, importantly, union thereof) containing a set of word-to-word entries, can be used in computing tsim.


In addition to the instantaneous description, each node contains a record of the alternatives previously tried (the edges to its child nodes in the tree) with their corresponding probabilities, plus a ranked list of the alternatives not yet tried.

we maintain the probability mass left unexplored in a node: the sum of the probabilities of the actions not yet tried.


They implemented a standard LR parser for CFGs, and a probabilistic method for conflict resolution similar to ours in that the decisions are conditioned to the LR states but with different methods.

they proceed in a parallel way accumulating probabilities along the paths and using a Viterbi decoder at the end.


The approach seems to have much yet to be explored, mostly to improve the accuracy side.

we have not yet come with a solid approach to lexicalization.


In constrast with many previous approaches (Brown et al., 1993;, our model does not try to capture how Source sentences can be mapped into Target sentences, but rather how Source and Target sentences can be generated simultaneously.

in the style of Melamed (2001), we estimate a joint probability model that can be easily marginalized in order to yield conditional probability models for both source-to-target and target-tosource machine translation applications.


Before the EM training procedure starts, one has no idea what word/phrase pairs are likely to share the same meaning.

all alignments that can generate a sentence pair (E, F) can be assumed to have the same probability.


In all instances, the MBR decoder tuned for each loss function was the best performing decoder under the corresponding error rate.

we note that alignment performance as measured under the AER metric can be improved by using MBR instead of ML alignment.


We observe some affinity among the loss functions.

the ML decoder performs better under the AER than any of the MBR-GAE decoders.


The main idea is that using a multi-parallel corpus -one that supplies several alternative verbalizations for each semantic expression -can enhance both the accuracy and the expressiveness of the resulting dictionary.

matching a semantic expression against a composite of the common structural features of a set of verbalizations ameliorates the effect of "mutations" within individual verbalizations.


Hence, we engage in crossinstance alignment to merge information about the predicate.

we align the slotted lattices for all instances of the predicate (see Figure 6); the resultant unified slotted lattice reveals linguistic expressions common to verbalizations of different instances.


While in state s ∈ S and performing action a ∈ A, the learner receives a reward r(s, a), and advances to state s = δ(s, a).

to learn an optimal control strategy that maxi-mizes the cumulative reward over time, an evaluation function Q(s, a) is defined as follows: Q(s, a) is the immediate reward, r(s, a), plus the discounted maximum future reward starting from the new state δ(s, a).


In our current work, we are focusing on expanding system coverage to other domains.

we plan to investigate semi-automatic methods for extracting ontological knowledge from existing webpages and databases.


In the case of speech-driven retrieval, where users usually have specific information needs, it is feasible that users utter contents related to a target collection.

there is a great possibility that detected OOV words can be identified as index terms that are phonetically identical or similar.


However, since a) a single sound can potentially correspond to more than one word (i.e., homonyms) and b) searching the entire collection for phonetically identical/similar terms is prohibitive, we need an efficient disambiguation method.

in the case of Japanese, the homonym problem is multiply crucial because words consist of different character types, i.e., "kanji," "katakana," "hiragana," alphabets and other characters like numerals 1 .


Finally, we produced a word/syllablebased trigram language model.

ooV words were modeled as sequences of syllables.


We use a DP matching method to identify the number of cases related to deletion, insertion, and substitution in  It should be noted that Equation 3is independent of the indexing method used, and therefore 2 can be any sequences of characters contained in E F .

any types of indexing methods (e.g., word-based and phrase-based indexing methods) can be used in our framework.


In addition, to minimize a score computation (for example, DP matching is time-consuming), it is desirable to delete terms that are associated with a diminished phonetic similarity value, ¤ ¦ ¥ % 3 § 2 © , prior to the computation of Equation 3.

an index file for query completion has to be organized so that a partial matching method can be used.


Looking at Table 1, one can see that recall was generally greater than precision.

our method tended to detect as many OOV words as possible.


The clusters were found automatically by attempting to minimize perplexity (Brown et al., 1992).

for predictive clustering models, we tried to minimize the perplexity of the training data of Letting N be the size of the training data, we have is independent of the clustering used.


Many of the causes listed above are related to verb phrases.

some of the adverb-related crossings (e.g.


In this paper, we present a similar system with a much simpler set of model parameters.

we compute the probability of a block sequence ¢ ¡ £ .


Next, we collect counts over aligned phrase sequences and use the relative frequencies to estimate the probability distribution in Table 2(2).

for smaller training corpora, unseen foreignlanguage phrase sequences are a problem, so we implemented a simple backoff method which assigns probability to translations of unseen foreign-language phrase sequences.


In spite of their considerable success, some shortcomings remain in the method used by Khudanpur and Kim (2002).

stochastic translation lexicons estimated using the IBM method (Brown et al., 1993) from a fairly large sentence-aligned Chinese-English parallel corpus are used in their approach -a considerable demand for a resourcedeficient language.


The local alignment we construct fits the characteristics of the data we are considering.

we adapt it to our framework to allow many-to-many alignments and some flips of order among aligned sentences.


In making the comparison between detecting EEs during pre-processing versus parsing, we are not only concerned with the accuracy of parsing, EE detection and antecedent recovery, but also with the running time of the parsers.

dienes and dubey (2003) found that detecting EEs is infeasible with an unlexicalized parser: the parser was slow and inaccurate at EE detection.


While this particular model was not able to outperform the pre-processing method, it can be further developed into a parsing model which can handle nonlocal dependencies by incorporating the local cues we found relevant.

a local window of five words, accompanied by the gap+ threads proved to be crucial.


In this paper, we find that the use of deep linguistic representations to predict these semantic labels are more effective than the generally more surface-syntax representations previously employed (Gildea and Palmer (2002)).

we show that the syntactic dependency structure that results load arg0 John arg2 hay arg1 truck Figure 1: PropBank-style semantic representation for both John loaded the truck with hay and John loaded hay into the truck from the extraction of a Tree Adjoining Grammar (TAG) from the PTB, and the features that accompany this structure, form a better basis for determining semantic role labels.


The entire set of semantic roles that are found in the PropBank are not used in our experiments.

we only include as semantic roles those instances in the propbank such that in the extracted TAG they are localized in the same elementary tree.


For this reason, we smooth the emit probabilities P (w|t) in the trigram model using distributional similarity following Chen (2001).

we use Jaccard's coefficient as the similarity metric with a similarity threshold of 0.04 and a radius of 25 because these were found to attain optimal results in Chen (2001).


The version of LDA that is used in this work differs from Srinivas (1997) in that there are other constraints on the linking process.

3 a link is not established if its existence would create crossing brackets or cycles in the dependency tree for the sentence.


In part this is due to the nature of the PropBank corpus that we are using.

because not all predicates in our version of the PropBank are annotated with semantic roles, the supertagger for SEM-TAG will sometimes annotate text without semantic roles when in fact it should contain them.


While the CCGbank is derived from the Penn Treebank, in many cases the constituent structures do not correspond.

there may be no constituent in the CCG derivation corresponding to the same sequence of words as a particular constituent in the Treebank tree.


The probabilities P (r i |F i , p) are combined with the probabilities P ({r 1..n }|p) for a set of roles appearing in a sentence given a predicate, using the following formula: This approach, described in more detail in Gildea and Jurafsky (2002), allows interaction between the role assignments for individual constituents while making certain independence assumptions necessary for efficient probability estimation.

we assume that sets of roles appear independent of their linear order, and that the features F of a constituents are independent of other constituents' features given the constituent's role.


In generating the CCGbank, various heuristics were used to make this distinction.

for PPs, it depends on the "closely-related" (CLR) function tag, which is known to be unreli-able.


In the distribution-based feature representation, we take each target noun in turn and compare its amalgamated value for each unit feature with (a) the values for other target nouns, and (b) the value of other unit features within that same feature cluster.

we focus on the relative prominence of features globally within the corpus and locally within each feature cluster.


singular determiners), each unit feature f s for target noun w is translated into 3 separate feature values: where freq( * ) is the frequency of all words in the corpus.

for each unit feature we capture the relative corpus frequency, frequency relative to the target word frequency, and frequency relative to other features in the same feature cluster.


Elementary system combination is achieved by voting between the three pre-processor outputs as to whether the target noun belongs to a given countability class.

the target noun is classified as belonging to a given countability class iff at least two of the preprocessors furnish linguistic evidence for membership in that class.


The differential in F-score for the best-N configuration as compared to the full feature set is statistically insignificant for N > 100 for countable nouns and N > 50 for uncountable nouns.

feature selection facilitates a relative speed-up of around 30× without a significant drop in F-score.


Each slice contain two pairs of parses from every sentence.

slice i contains positive samples ((p k , p ki ), +1) and negative samples ((p ki ,p k ), −1), wherep k is the best parse for sentence k, p ki is the parse with the ith highest loglikelihood in all the parses for sentence k and it is not the best parse (Shen and Joshi, 2003).


Since HP-Obj had produced roughly 17,000 objective sentences used for training, we used the 9,500 new subjective sentences along with 7,500 of the previously identified subjective sentences as our new training set.

the training set that we used during the second bootstrapping cycle contained exactly the same objective sentences as the first cycle, half of the same subjective sentences as the first cycle, and 9,500 brand new subjective sentences.


co-training algorithms are fundamentally different.

goldman and Zhou rely on hypothesis testing to select new instances to add to the labeled data.


The class distributions in coreference data sets are skewed.

the fact that most NP pairs in a document are not coreferent implies that the negative instances grossly outnumber the positives.


The intuition behind the B&M co-training algorithm is to train two classifiers that can help augment each other's labeled data by exploiting two separate but redundant views of the data.

each classifier is trained using one view of the labeled data and predicts labels for all instances in the data pool, which consists of a randomly chosen subset of the unlabeled data.


each classifier always selects the most confidently labeled instances to add to the other's labeled data at each iteration.


We focus on descriptive texts which realize a single, purely informative, communicative goal, as opposed to cases where more knowledge about speaker intentions are needed.

we present experiments on biographical descriptions, where the planned system will generate short paragraph length texts summarizing important facts about famous people.


The knowledge base contains many pieces of information related to the person the biography talks about (and that the system will use to generate that type of biography), not all of which necessarily will appear in the biography.

the associated knowledge base is not the semantics of the target text but the larger set 1 of all things that could possibly be said about the person in question.


In order to compare language models, we turned to techniques from adaptive NLP (i.e., on the basis of genre and type distinctions) (Illouz, 2000).

we employed the cross entropy 4 between two language models ¡ ¡ and ¡ ¢ , defined as follows (where is the probability that to be a model of randomly selected documents and ¡ ¢ a model of a subset of texts that are associated with the cluster, then a greater-than-chance value would be an indicator that the cluster in the semantic side is being correlated with changes in the text side.


However, the expressivity of the class-based approach is plainly not enough to capture the idiosyncrasies of content selection: for example, it may be the case that children's names may be worth mentioning, while grand-children's names are not.

in Figure 3, E r elative person name firstF is dependent on E r elative TYPEF and therefore, all the information in the current instance should be taken into account to decide whether a particular data path and it values should be included or not.


For the U-test selected paths, the situation is more complex, as we only have clues about the importance of the data path as a whole.

while we know that a particular data path is relevant to our task (biography construction), we don't know with which values that particular data path is being verbalized.


This method can only capture the global reliability of all the features.

only the reliability of the training examples as a whole can be captured.


We considerp(y|h i (x) > 0) as the parameter θ of the Bernoulli trials.


At this point, the 2-norm extension shows the advantage of being robust, especially for the Reuters dataset.

the 2-norm models outperform the normal inequality models in the evaluation set.


One difference here from the original IFS algorithm is that we adopt a technique in (Goodman, 2002) for optimizing the parameters in the conditional ME training.

we use array z to store the normalizing factors, and array sum for all the un-normalized conditional probabilities sum [i, y].


A number of experiments have been conducted to verify the rationale behind the algorithm.

we would like to have a good understanding of the quality of the selected features using the SGC algorithm, as well as the amount of speedups, in comparison with the IFS algorithm.


Interpreting the word representations learned in this framework: For example, word clustering, context clustering, etc.

if we use separate mapping matrices for word/NT/POS at different positions in the context, we may be able to learn very different representations of the same word/NT/POS.


This might be due to the fact that the label distribution of the training data is more similar to Test .

there are many new nouns in Test p U that belong to "abstract" classes 6 , which seem harder to learn.


We obtained a 9.1 percentage point improvement in precision (an 18% improvement relative to the F1 feature set) and a 12.8 percentage point improvement in recall (32% improvement relative to F1), which amounts to an 11.4 percentage point improvement in -measure (25% improvement relative to F1 feature set).

all the examples in this paper were resolved.


For this reason, using the Web has proved successful in several other fields of NLP, e.g., machine translation (Grefenstette, 1999) and bigram frequency estimation (Keller et al., 2002).

(Keller et al., 2002) have shown that using the Web handles data sparseness better than smoothing.


In the first part of this paper, we discuss these language-specific issues in Chinese NE recognition.

we use a hidden Markov model (HMM) system as an example, and discuss various issues related to applying the HMM classifier to Chinese.


We explored various ways to combine the results from the four classifiers.

for the first part of the experimental setup, we consider the following classification framework: given the (probabilistic) output , the classifier combination problem can be viewed a probability interpolation problem -compute the class probability distribution conditioned on the joint classifier output: where # is the i th classifier's output, is an observable context (e.g., a word trigram) and each of the classifiers votes with equal weight for the class that is most likely under its model, and the class receiving the largest number of votes wins (i.e., it is selected as the classification output).


(2002) proposes to use a class-based model for Chinese NE recognition.

it uses a character-based trigram model for the class person, a word-based model for the class location, and a more complicated model for the class organization.


It is, however, important to investigate this approach by which it is expected that we can alleviate the cost of corpus annotation.

we focus on virtual examples with Support Vector Machines, introduced by Vapnik (1995).


Furthermore, we carried out another experiment with a combined method to create two virtual examples with GenerateByDeletion and GenerateByAddition respectively.

four virtual examples were generated from a Support Vector.


The feature values were calculated for each extracted unit in the training and the validation sets, that is for the n-grams, NP-chunks, stemmed NPchunks, patterns, and the stemmed patterns respectively.

the within-document frequency, the collection frequency, and the proportion of the document preceding the first appearance for each potential term were calculated.


Our formulation uses a factorization analogous to the standard dynamic programs for parsing.

it allows one to efficiently learn a model which discriminates among the entire space of parse trees, as opposed to reranking the top few candidates.


Tag-word production weights were fixed to be the log of the generative P (w|t) model.

the only change between generative and basic is the use of the discriminative maximum-margin criterion in place of the generative maximum likelihood one.


We thus use discriminants as the basis of calculating annotation cost to evaluate the effectiveness of different experiment AL conditions.

we set the cost of annotating a given sentence as the number of discriminants whose value were set by the human annotator plus one to indicate a final 'eyeball' step where the annotator selects the best parse of the few remaining ones.


First, we restrict attention to a finite part of the complete state.

we map particular elements of the parser's data structures onto a feature vector.


Previous work  has reported that the distribution of the number of examples per word sense (bias for short) has a strong influence in the quality of the results.

the results degrade significantly whenever the training and testing samples have different distributions of the senses.


To this end substantial improvements are needed in order to promote scalability relative to current Webbased approaches.

we address two major goals: reducing dramatically the complexity of required auxiliary inputs, thus enabling to apply the methods at larger scales, and generalizing the types of structures that can be acquired.


In each, he combines Bagging, using the CART methodology to create trees, with random feature selection (Amit and Geman, 1997) at each node in the tree.

at each node he selects a different random subset of the input features and considers only these in establishing the decision at that node.


In this experiment, we preprocess the baseline in-puts described in Figure 2 to reduce their number of category values.

for each original category value, v i ∈ V , we determine whether it occurs in observations associated with one or more than one semantic role label, R. If it is associated with more than one R, v i is left as is.


Semantic annotation of text corpora is needed to support tasks such as information extraction and question-answering (e.g., Riloff and Schmelzenbach, 1998;Niu and Hirst, 2004).

labelling the semantic roles of the arguments of a verb (or any predicate), as in (1) and (2), provides crucial information about the relations among event participants.


For our first backoff level, we introduce a novel way to generalize over the verb, slot, and noun information of Here we use a linear interpolation of three probabilities, each of which: (1) drops one source of conditioning information from the most specific probability, and (2) generalizes a second source of conditioning information to a class-based conditioning event.

we use the following probability formula: is verb class, and the individual probabilities are (currently) equally weighted (i.e., all , the noun component is dropped, and the slot is generalized to the appropriate slot class.


There is a unique initial state p, a unique final state q, and a state for each possible document phrase.

for all 1 ≤ i ≤ i ≤ n, there is a state that corresponds to the document phrase beginning at position i and ending at position i , d i i , which we will refer to as r i,i .


Annotators were asked to perform phrase-tophrase alignments between abstracts and documents and to classify each alignment as either possible P or sure S, where P ⊆ S. In order to calculate scores for phrase alignments, we convert all phrase alignments to word alignments.

if we have an alignment between phrases A and B, then this induces word alignments between a and b for all words a ∈ A and b ∈ B.


Given an alignment A, we could calculate precision and recall as (see (Och and Ney, 2003)): One problem with these definitions is that phrasebased models are fond of making phrases.

when given an abstract containing "the man" and a document also containing "the man," a human may prefer to align "the" to "the" and "man" to "man."


However, these numbers are misleading as the rating scale is ordinal and almost all the disagreements were the result of grading a story either one rank better or worse.

rater A usually marked incomplete stories as poor while the other raters assigned partial credit.


By adopting at the outset a paradigm geared toward generating sentences, this approach overcomes many problems encountered by task-specific approaches.

we show that SMT techniques can be extended to paraphrase given sufficient monolingual parallel data.


Consider the third and fourth rows of Table  3, which indicate the extent of embellishment and lossiness found in MSA paraphrases and the topranked PR paraphrases.

noteworthy is the lossiness of MSA seen in row 4.


The most obvious need for this comes from the fact that the sense-conditional word model employs relative position feature weighting, which can change the relatively magnitude of the probabilities in this term.

by using feature weights greater than 1.0 during inference we overestimate the actual amount of evidence.


We experimented with some of the string kernels proposed in (Lodhi et al., 2000;Leslie and Kuang, 2003), which have been shown to perform very well for indicating string similarity in other domains.

we applied the N-gram kernel, Subsequence kernel, and Wildcard kernel.


The resulting potential function g(c) is the indicator function on the interval [−γ, γ].

it restricts the allowable amount of constraint relaxation to at most ±γ.


Our emulation of Collins' Model 2 (hereafter referred to simply as "the model") has eleven parameter classes, each of which employs up to three back-off levels, where back-off level 0 is just the "un-backed-off" maximal context history.

1 a smoothed probability estimate is the interpolation of up to three different unsmoothed estimates.


The conclusion was that the minuscule drop in performance from removing bigrams must have been due to the fact that they were barely able to be used.

it appeared that bigram coverage was not nearly good enough for bigrams to have an impact on parsing performance, seemingly confirming the prevailing view.


To answer this question, we used our parser to constrain-parse its own output.

having trained it on Sections 02-21, we used it to parse Section 00 of the Penn Treebank (the canonical development test set) and then re-parse that section using its own highestscoring trees (without lexicalization) as constraints, so that it only pursued theories consistent with those trees.


From a much more purely engineering perspective, one can also use the analysis of the previous two sections to identify individual parameters that carry little to no useful information and simply remove them from the model.

if p b is a particular distribution and p b+1 is its corresponding back-off distribution, then one can remove all parameters p b such that where 0 < t < 1 is some threshold.


Let y be a path, a sequence of tokens where each token is a pair of word w i and its part-of-speech t i .

where #y is the number of tokens in the path y.


The model we use is agnostic to any sort of depth: it hypothesizes underspecified tags and allows the matching bracket constraint to select a solution.

we approach the NP Bracketing problem as a tagging and reranking problem.


For instance, the tag sequence sing sing could correspond either to Nevertheless, due to the constraints involved in the tagging process (namely that a close tag cannot appear unless one is already within an NP and that one cannot have two close tags when the corresponding open tags appear at the same location 1 ), we hope that our system will be able to disambiguate sufficiently.

although our taggings are under-specified, we hope that the additional constraints that we subsequently associate with these tags will yield high quality bracketings.


Markov 2 Rules: CFG rules where 2-level Markovization has been applied.

we look at the rule for generating the first two tags, then the next two (given the previous one), then the next two (given the previous one), and so on.


All the COL03 systems are results obtained using the restriction of the output of Collins (2003) parser.

the two comparable numbers coming from Collins' parser are COL03 N P and COL03 F ull .


The CHUNK row in the results table is the performance of an optimally performing NP chunker.

this is the performance attainable given a chunker that identifies base NPs perfectly (at 100% precision).


To a large extent, knowledge-poor methods suffice to yield good recognition performance.

supervised learning can be used to produce a system with performance at or near the state of the art (Bikel et al., 1997).


In this case, the loss function is: with the Kronecker delta δ(c,c).

the errors are counted at the string level and not at the level of single symbols.


Thus we have obtained the Bayes decision rule for minimum symbol error at position m = 1, ..., N : By construction this decision rule has the special property that it does not put direct emphasis on local coherency of the POS tags produced.

this decision rule may produce a POS tag string which is linguistically less likely.


For the direct model, the overall tagging error rate increases on each of the two tasks (from 3.0 % to 3.3 % on WSJ and from 5.4 % to 5.6 % on MTP) when we use the symbol decision rule instead of the string decision rule.

for OOVs, the error rate goes up clearly.


The testing algorithm is similar to that described in Section 3.2, except that the probability of a word being assigned a POS tag t is estimated by the product of the probability of its individual characters being assigned the same POS tag t. For example, when estimating the probability of "新华社" being tagged NR, we find the product of the probability of "新" being tagged NR, "华" being tagged NR, and "社" being tagged NR.

we enforce the constraint that all characters within a segmented word in the presegmented input sentence must have the same POS tag.


In this paper we discuss using machine learning methods to classify email according to the intent of the sender.

we classify emails according to an ontology of verbs (e.g., propose, commit, deliver) and nouns (e.g., information, meeting, task), which jointly describe the "email speech act" intended by the email sender.


The principle states that the entropy rate in a text is constant, i.e., that speakers produce sentences so that on average, all sentences in a text have the same entropy.

communication is optimal in the sense that all sentences in the text are equally easy to understand, as they all have the same entropy.


In the eye-tracking literature, it is generally recommended to run regression analyses on the reading times collected from individual subjects.

it is not good practice to compute regressions on average reading times, as this fails take betweensubject variation in reading behavior into account, and leads to inflated correlation coefficients.


As we argued in Section 2, this is predicted by the entropy rate principle: the optimal way to send information is at a constant rate.

speakers should produce sentences with constant informativeness, which means that if context is taken into account, all sentences should be equally difficult to process, no matter which position they are at.


Moreover, with a slope of very close to 1, the LLR score and negative log p-values are not merely correlated, they are virtually the same except for the small delta represented by the intercept.

p-value ≈ e  would seem to be not too bad an approximation.


The best results were obtained between English and Spanish language pairs, in which the human translator would need to type less than 25% of the total reference sentences.

this would result in a theoretically factor of 4 increase in the productivity of human translators.


This information can be used in the HMM framework by applying Bayes rule to compute P (o|c) = P (c|o)P (o) where P (o) and P (c) are the probabilities of observing o and being at c, respectively.

instead of estimating the observation probability P (o|c) directly from training data, we compute it from the classifiers' output.


The experiments discussed in section 4 are completely devoid of linguistic knowledge.

experiment B inherently assumes that any sequence of three consonants can be the root of a given word.


As is well-known from other learning tasks, fine-tuning of the feature set can produce additional accuracy; we expect this to be the case in this task, too.

introducing features that capture contextual information is likely to improve the results.


And the inconsistency of NE translation and NER in different languages is also a big problem.

in Chinese NE processing, since Chinese is not a tokenized language, previous work (Huang et al., 2003) normally conducts word segmentation and identifies Named Entities in turn.


The basic idea underlying DRE is to combine the knowledge in WORDNET DOMAINS and a probabilistic framework which makes use of a large-scale corpus to induce domain frequency distributions.

given a certain domain, DRE considers frequency scores for both relevant and non-relevant texts (i.e.


In Section 3 the problem of estimating domain relevance for a text is introduced.

section 4 briefly sketchs the WsD system used for evaluation.


WORDNET DOMAINS is an extension of WORD-NET (version 1.6) (Fellbaum, 1998), in which each synset is annotated with one or more domain labels, selected from a hierarchically organized set of about two hundred labels.

issues concerning the "completeness" of the domain set, the "balancing" among domains and the "granularity" of domain distinctions, have been addressed.


It is important to have a method at hand that gives us assurances that the observed increase in the test score on a test set reflects true improvement in system quality.

we need to be able to gauge, if the increase in score is statistically significant.


In this paper, we introduce TextRank-a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications.

we propose two innovative unsupervised methods for keyword and sentence extraction, and show that the results obtained compare favorably with previously published results on established benchmarks.


In this paper, we introduced TextRank -a graphbased ranking model for text processing, and show how it can be successfully used for natural language applications.

we proposed and evaluated two innovative unsupervised approaches for keyword and sentence extraction, and showed that the accuracy achieved by TextRank in these applications is competitive with that of previously proposed state-of-the-art algorithms.


Model g is a reduced model with the statistics of the decoder without using the correct translation WFST.

g reduces the states of the fullexpansion model more roughly than .


The matrix V T describes the importance degree of each 'implicit topic' in each sentence: the summarization process simply chooses the most informative sentence for each term.

the kth sentence chosen is the one with the largest index value in the kth right singular vector in matrix The summarization method proposed by Gong and Liu has some disadvantages as well, the main of which is that it is necessary to use the same number of dimensions as is the number of sentences we want to choose for a summary.


An alternative approach is to use SVD to identify 'topics' on the basis of two types of 'terms': terms in the lexical sense (i.e., words) and terms in the sense of objects, which can be represented by anaphoric chains.

our representation of sentences would specify not only if they contain a certain word, but also if they contain a mention of a discourse entity (See Figure 1.)


If 1a and 2a refer to the same person, however, and 1b and 2b are nations in citizenship relations with 1a and 2a, then it would indeed be the rare case in which 1b and 2b refer to two different nations.

the relation of a nation to its citizens is one-to-many.


Interpretability: A good metric should be easy to interpret.

there should be an intuitive sense of how good a system is when a metric suggests that a certain percentage of coreference results are correct.


Figure 1 plots f-measure against the minimum amount of redundancy.

the value at X=3 gives the f-measure averaged over all people who were mentioned at least thrice in the input.


The best performance is generated by L-CS-Local-Long.

it boosts CER by a relative improvement of 12.30% over CS and a relative improvement of 7.02% over L-CS-Local.


Moreover, the performance of error detection was improved incrementally as the scope of context was enlarged, and the best performance was achieved when sentence context was considered.

enlarging the context modeled by linguistic features improved the capability of error detection by finding more errors without deteriorating and even improving the precision.


Our algorithm tries to detect recognition errors by identifying and filtering semantic outliers in the transcripts.

it declares as recognition errors all the words with low semantic similarity to other words in the transcript.


In future work, we plan to run our experiments on other datasets when they become available to us.

we want to experiment with multi-topic audio documents where we expect more marked advantages for windowing and alternative aggregation schemes like MAX and 3MAX.


Our assumption that the loss function decomposes over the edges is crucial to solving this problem.

we use weighted Hamming distance, which counts the number of variables in which a candidate solutionȳ i differs from the target output y i , with different cost for false positives (c + ) and false negatives (c -): The loss-augmented matching problem can then be written as an LP similar to Equation 1 (without the constant term jk cy i,jk ): Hence, without any approximations, we have a continuous optimization problem instead of a combinatorial one: where is the constant term, F i is the appropriate matrix that has a column of features f (x i,jk ) for each edge jk, c i is the vector of the loss terms c + − (c -+ c + )y i,jk and finally Plugging this LP back into our estimation problem, we have where Instead of the derivation in Taskar et al.


The the-le error in Figure 1(c) is symptomatic of this lack.

it is a slightly better pair according to the Dice value than the correct the-les.


The LaSO framework relies on a monotonicity assumption: given a structure and a node " in the search space, we must be able to calculate whether it is possible for this node , where weights correspond to features of search space nodes and inputs.

we write as a function that takes a pair of an input and a node in the search space " and produces a vector of features.


Suppose that we are at the word "them" and the hypothesis we are expanding is correct.

we have correctly identified "Bill Clinton" with entity type "person" and mention type "name;" that we have identified "the Senate" with entity type "organization" and mention type "name;" and that we have identified both "The President" and "his" as entities with entity type "person" and mention types "nominal" and "pronoun," respectively, and that "The President" points back to the chain Bill Clinton and that "his" points back to the chain Bill Clinton, The President .


Based on the hypothesis that many name to nominal coreference chains are best understood in terms of background knowledge (for instance, that "George W. Bush" is the "President"), we have attempted to take advantage of recent techniques from large scale data mining to extract lists of such pairs.

we use the name/instance lists described by (Fleischman et al., 2003)  GBs of news data).


The inference-based features are computed by attempting to infer an underlying semantic property of a given mention.

we attempt to identify gender and semantic number (e.g., "group" is semantically plural although it is syntactically singular).


The operational definition of "new" here is information that has not appeared previously in this topic's set of documents.

we allow the system to assume that the user is most concerned about finding new information in this particular set of documents, and is tolerant of reading information he already knows because of his background knowledge.


The truth data was created by asking NIST assessors (the humans performing this task) to identify the set of relevant sentences from each relevant document and then from that set of relevant sentences, mark those that were novel.

the assessors were instructed to identify a list of sentences that were: 1. relevant to the question or request made in the description section of the topic, 2. their relevance was independent of any surrounding sentences, 3. they provided new information that had not been found in any previously picked sentences.


Different NED systems are compared based on their minimum cost.

the lower the C det score reported by a system on test data, the better the system.


The goal of this paper is to investigate additional principled unsupervised mechanisms within the initial classification step, applied to the text categorization.

(a) utilizing a Latent Semantic Space to obtain better similarity assessments between seeds and examples, and (b) applying a Gaussian Mixture (GM) algorithm, which provides a principled unsupervised estimation of classification probability.


We recall that mixture models have been widely used in pattern recognition and statistics to approximate probability distributions.

a well-known nonparametric method for density estimation is the so-called Kernel Method (Silverman, 1986), which approximates an unknow density with a mixture of kernel functions, such as gaussians functions.


The performance figures show the incremental benefit of both LSI and GM.

when starting with just initial seeds and do not exploit the LSI similarity mechanism, then the performance is heavily penalized.


We applied and evaluated our algorithm on some text categorization tasks and showed the contribution of the two techniques.

we obtain, for the first time, competitive performance using only the category names as initial seeds.


where S i is a possible subtree, # S i (T j ) is the number of times S i is included in T j , and W (S i ) is the weight of S i .

tree kernels are inner products in a subtree feature space where a tree is mapped to vector V (T j  With tree kernels we can take global structures into account, while alleviating overfitting with kernelbased learning methods such as support vector machines (SVMs) (Vapnik, 1995).


In these kernels, we require m(ψ(n i )) = m(n i ) in addition to l(ψ(n i )) = l(n i ) for subtree S i to be regarded as included in tree T j .

these kernels treat as the new label of node n i and sum only over subtrees that have at least one marked node.


They reported that ROUGE-N performed well as regards automatic summarization.

rOUGE-1, i.e., unigram matching, provides the best correlation with human evaluation.


4 Our templates for handling multi-word correspondences are grounded in the outcome of this finding.

we make the (frequently correct) assumption that at least one alignment link in a many-to-many correspondence is correctly identified in the initial 3 This is a well-known characteristic of statistical alignment systems-motivated by the need to ensure a target-word translation ei for each source word fj while modeling p(F |E) -for downstream MT.


That is, we make the (frequently correct) assumption that at least one alignment link in a many-to-many correspondence is correctly identified in the initial 3 This is a well-known characteristic of statistical alignment systems-motivated by the need to ensure a target-word translation ei for each source word fj while modeling p(F |E) -for downstream MT.

4 we conducted a preliminary study using 40 manually-aligned English-Spanish sentences from a mixed corpus (UN + Bible + FBIS) as our gold standard.


Using these features is what sets ALP apart from systems like the RA approach.

three features are used to instantiate the templates: • POS tags on both sides: We assign POS tags using the MXPOST tagger (Ratnaparkhi, 1996) for English and Chinese, and Connexor for Spanish.


5 If both POS tags and dependency relations are available, they can be used together to instantiate the templates.

a word can be instantiated in a TBL template with: (1) a POS tag (e.g., Noun, Adj); (2) a relation (e.g., Subj, Obj); (3) a parameter class (e.g., Change of State); or (4) different subsets of (1)-(3).


We see that all discriminative training methods outperform MLE significantly (p-value < 0.01).

mSR outperforms mLE by more than 20% CER reduction.


Finally, we find that in most adaptation test sets, MSR achieves slightly better CER results than the two competing discriminative methods.

the improvements of MSR are statistically significant over the boosting method in three out of four domains, and over the perceptron algorithm in the Yomiuri domain.


Therefore, this paper addresses how to use information from one modality (e.g., deictic gesture) to directly influence the semantic processing of another modality (e.g., spoken language understanding) even before the fusion stage.

we present a new salience driven approach that uses gesture to influence spoken language understanding.


This salient part of the physical world will prime what users tend to communicate in speech and thus in turn can be used to constrain hypotheses for spoken language understanding.

this approach incorporates a notion of salience from deictic gestures into language models for spoken language processing.


Here, we do not address the acoustic model of speech recognition, but rather incorporate the salience distribution for language modeling.

our focus is on investigating the effect of incorporating additional information from other modalities (e.g., gesture) with traditional language models.


Equation 4indicates that only information associated with the salient entities is used to estimate the word class distribution.

the word class probability favors the salient physical world as indicated by the salience distribution ) ( k t e i k e ) (e P i t v .


In these studies, users interacted with our multimodal interface using both speech and deictic gestures to find information about real estate properties.

each user was asked to accomplish five tasks.


The distinction between hearer-old and hearer-new entities depends on the readers.

we are attempting to automatically infer which characters would be hearer-old for the intended readership of the original reports, which is also expected to be the intended readership of the summaries.


However largely for the sake of technical convenience, we assume it is one component of a multinomial distribution known as the Dirichlet distribution.

we talk about Dirichlet(θ θ θ|v v v), namely a Dirichlet posterior of θ, given observations v v v, where θ θ θ = (θ 1 , .


We compared the performance of discriminative methods against a MAP estimation method as the baseline, in this case the linear interpolation method.

we created a word trigram model using the adaptation data for each domain, which was then linearly interpolated at the word level with the baseline model.


In evaluating both MAP estimation and discriminative models, we used an N-best rescoring approach.

we created N best hypotheses using the baseline trigram model (N=100 in our experiments) for each sentence in the test data, and used adapted models to rescore the N-best list.


In this figure, a model is considered to have fewer side effects when the ER is smaller at the same RER (i.e., smaller value of y for a fixed value of x), or when the RER is larger at the same ER (i.e., larger value of x at the fixed y).

the closer a model is plotted to the corner B of the graph, the better the model is; the closer it is to the corner A, the worse the model is.


When plotted on the RER/ER graph in Figure 5, however, it is clear that the overfit model β has the worse ER than the non-overfit counterpart α.

models α and β have the same CER, but they are not equivalent: model β is not only worse in light of containing more features, but also in terms of causing more side effects.


To return goal, one may not need to compute the values of all items.

9 finding the best parse should not require finding all parses (in contrast to Goodman (1999) and Zhou and Sato (2003)).


Luckily the same program serves, since for such models, the E count (expected count) of an item a can be found as a • g(a)/goal.

the inside-outside algorithm has the same structure as computing the function and its gradient.


Systems participating in the TREC evaluation were required to process series independently from one another, and were required to process an individual series in question order.

systems were allowed to use questions and answers from earlier questions in a series to answer later questions in that same series, but could not "look ahead" and use later questions to help answer earlier questions.


As these are types of subjective expressions, to create the corpus, we just needed to manually annotate the existing subjective expressions with their contextual polarity.

we developed an annotation scheme 3 for marking the contextual polarity of subjective expressions.


We investigate two very different learning-based methods from information extraction for the problem of opinion source identification: graphical models and extraction pattern learning.

we consider Conditional Random Fields (Lafferty et al., 2001) and a variation of AutoSlog (Riloff, 1996a).


(3) And for this reason, too, they have a moral duty to speak out, as Swedish Foreign Minister Anna Lindh, among others, did yesterday.

(4) Iran and Iraq are at loggerheads with each other to this day.


In fact, the subtask of entity mention detection itself is a very challeng- ing subtask since respective expressions can have relatively complex syntactic and categorical ("semantic") structures.

entity mentions in a body of text can occur in relatively complex embedded constructs with many attributes.


We argue that data sparsity and computational complexity is not as severe as it might be expected in the other machine learning methods that are based on maximum likelihood parameter estimation.

we claim that the large set of classification labels and training data sparseness are not major drawbacks.


In this example, it's important to try to match relationships as well as words.

any answer to the question should preserve the dependency between Israel and established.


Our VertexSub(v, M (v)) model is based upon a sliding scale, where progressively higher costs are given based upon the following conditions:  (Fellbaum, 1998).

we use the top 3 senses of both words to determine synsets.


However, the improvements for PN, LN and ON are different.

the POS Model has obvious side-effect on the recall of ON recognition at all times, while the recalls for PN and ON recognition are improved in the beginning but decreased in the ending with the increasing of ζ.


Sequential learners of the sort used here classify tokens from each document independently; moreover, the classification of a word w is independent of the classification of other occurrences of w elsewhere in the document.

the fact that a word w has appeared somewhere in a context that clearly indicates that it is a name does not increase the probability that it will be classified as a name in other, more ambiguous contexts.


The noisy dictionary-matching scheme is susceptible to false positives.

some words predicted by the extractor to be names are in fact non-names.


For example, to determine that a news story is on the 2004 elections in the United States, the words President Bush, John Kerry and USA are necessary features of the story.

names of people, places and organizations are key entities of a news story.


We also grouped together names that differ by a string edit distance of one, giving a fifth system.

we use the Levenshtein distance (Levenshtein, 1966), that is the number of insertions, deletions and substitutions needed to convert one string to the other.


For the Generative Unsupervised and Generative Supervised methods, we use the same models, but in this case the training set consists of pairs of words obtained from the ASR and closed caption text as opposed to sentences.

the place of words in the previous case is taken by characters.


A key disadvantage of the CMM is that it makes critical statements about independence that the HMM does not: the converging arrows at each tag put the parent nodes (the previous tag and the current observation) into causal competition and as a result the model states that the previous tag is independent of the current observation.

all states (tags) are independent of future observations (words).


We thus propose a new solution to the problem, which retains the discriminative conditional form of "tag given word" from the CMM, but avoids label bias by temporally linking adjacent tags in a new way.

we employ virtual evidence in the form of a binary observed child node, c i , between adjacent tags (Figure 3) or a windowed sequence of tags.


If we were to naïvely add c i = 1 to all samples of the training data, the model would learn that c i is constant rather than random, and therefore that it is independent of its parents, s i−1 and s i .

this naïvely-trained model would assume that P (c i = 1|s i−1 , s i ) = 1 ∀ (s i−1 , s i ), and when used to tag the sentences in the test-set (also labeled with c i = 1), it would maximize this simplified joint probability in which the relationship between s i−1 and s i has been lost: In order to induce and thereby have the model learn the relationship between the adjacent tags s i−1 and s i , the training has to be modified to include samples that are labeled with c i = 0.


We believe that this model holds great promise for other NLP tasks as well as in other applications of machine-learning such as computational biology.

the way it factorizes the joint probability into a "horizontal" component which connects various nodes to the virtualevidence node, and a "vertical" component (used here to link a tag to a set of observations), provides great simplicity, flexibility, and power.


In this paper, the dictionary defines the support set of the channel model.

p c (x | y) > 0 if and only if y ∈ d(x).


We estimate the source conditionally from annotated data.

we maximize wherep(•, •) is the empirical distribution defined by the training data and θ are the model parameters.


3) What is the best way to select a corpus for mining?

is it enough to mine only within the same collection that is involved in retrieval, clustering or other processing ( target collection), or constructing and mining a larger external corpus (like a subset of World Wide Web) would be of much greater help?


Models with widely varying behavior can be created by changing the way these domains are defined.

in this section we will see how different definitions of the domains give rise to the three main model types: clustering, refinement, and mapping into a pre-built ontology such as WordNet.


However, non-projective analyses have recently attracted some interest, not only for languages with freer word order but also for English.

wang and Harper (2004) describe a broad coverage non-projective parser for English based on a hand-constructed constraint dependency grammar rich in lexical and syntactic information.


Furthermore, the resulting parsing algorithms are more efficient than lexicalized phrase structure approaches to dependency parsing, allowing us to search the entire space without any pruning.

the non-projective parsing algorithm based on the Chu-Liu-Edmonds MST algorithm provides true non-projective parsing.


In this paper, we explore the applicability of computational approaches to the recognition of verbally expressed humor.

we investigate whether automatic classification techniques are a viable approach to distinguish between humorous and non-humorous text, and we bring empirical evidence in support of this hypothesis through experiments performed on very large data sets.


The non-humorous data is selected such that it is structurally and stylistically similar to the oneliners.

we use three different negative data sets: (1) Reuters news titles; (2) proverbs; and (3) sentences from the British National Corpus (BNC).


We tried to identify a set of features that were both significant and feasible to implement using existing machine readable resources.

we focus on alliteration, antonymy, and adult slang, which were previously suggested as potentially good indicators of humor (Ruch, 2002;Bucaria, 2004).


In addition to stylistic features, we also experimented with content-based features, through experiments where the humor-recognition task is formulated as a traditional text classification problem.

we compare results obtained with two frequently used text classifiers, Naïve Bayes and Support Vector Machines, selected based on their performance in previously reported work, and for their diversity of learning methodologies.


(anonymous one-liner) The creative genres of natural language have been traditionally considered outside the scope of any computational modeling.

humor, because of its puzzling nature, has received little attention from computational linguists.


Together, these results indicate the importance of algorithm and parameter selection for comparative empirical NLL studies.

our results show the danger of relying on the same one or two algorithms for all tasks.


Both of these metrics measure the degree that predicted coreference chains agree with an answer key.

they measure the chain-level precision and recall (and the corresponding F-measure).


For this reason we measure the performance with an unbalanced Fmeasure that emphasizes precision.


We set a threshold of 100, and only when the amount of snippets retrieved from Xinhua News is smaller than 100, do we turn to Central News to collect more data.

for 48 out of the 223 (22%) Chinese queries, the system retrieved less than 100 instances from Xinhua News so it extracted more data from Central News.


This is also a possible solution to the problem that ambiguity may be preserved between Chinese and English.

when a Chinese translation of an English sense is still ambiguous, we could try to collect sense examples using translation in a third language, Japanese, for instance.


The query language which exactly matches the expressive power of regular tree grammars is MSO.

a set of trees is definable by a regular tree grammar iff there is an MSO formula that defines this set of trees (Gécseg and Steinby, 1997).


The output is an analysis of the automaton that is constructed.

it is stated whether  Automaton for q and t or / 0 Figure 1: Method of using MONA for querying.


Since each tree is queried separately, we can expect a linear time increase in the query time in the number of trees.

evaluating the query on the whole treebank would probably take about 1 hour.


By and large, confusions occur (at least in humans) between phones with phonological features in common (e.g., (Miller and Nicely, 1955)).

three features (voicing, manner, and place) have been postulated as relatively invariant (see e.g., (Stevens, 1981), quoted in (Church, 1987)).


Since we are only investigating the combined acoustic-phonetic-lexical representation, we have left out the re-weighting and pruning of hypotheses due to integration of a language model, discourse model, or any other constraints.

this model consists of three finite state transducers composed.


This approach gave improved RC performances for both the Remedia and ChungHwa corpora, attaining HumSent accuracies of 42% and 69% respectively.

performance analysis based on Remedia shows that relative performances of 20.7% is due to metadata matching and a further 10.9% is due to the application of Web-derived answer patterns.


Our current work attempts to incorporate a similar idea in exploiting Web-derived knowledge to aid RC.

we have devised an automatic acquisition process for Web-derived answer patterns.


We obtain improvements over previously reported results for Remedia, with an overall HumSet accuracy of 42%.

a relative gain of 20.7% is due to metadata matching and a further 10.9% is due to application of Webderived answer patterns.


We obtain state-of-the-art results both in function labelling and in parsing, by automatically relabelling the Penn Treebank trees.

we obtain the best published results on semantic function labels.


NULL-labelled constituents are only needed to calculate the precision and recall of other function labels.

(NULL-labelled constituents never contribute to the numerators of our calculations.)


8 Comparison to other researchers (last three lines of Table 4) shows that we achieve state-of-the-art results with a single integrated model that is jointly optimised for all the different types of function labels and for parsing, while previous attempts are optimised separately for the two different sets of labels.

our method performs better on semantic labels.


Model 1 makes use only of the partial tree defined by base chunks and clause boundaries, while Model 2 explores full parse trees.

the sequential tokens are selected as follows.


In this paper, linguistic knowledge is used to guide us to design features in maximum-entropy-based coreference resolution systems.

we show how to compute a set of features to approximate the linguistic notions such as governing category and apposition, and how to compute the dependency features using syntactic parse trees.


Our final system achieves a BLEU score of .333, as compared to .270 for the baseline word-to-word system.

in a statistical machine translation task, the goal is to find the most probable translation of some foreign language text f into the desired language e. the system seeks to maximize P (e|f ).


The important point is that we are now dealing with approximate quantities which depend continuously on θ .

a( θ ) ≈Ã( θ ), whereÃ Since the marginal total of positive instances n pos (cf.


This technique is quite general and well suited for working with other quantities that can be expressed in terms of hits, misses, false alarms, correct rejections, etc.

it could be used to find a point estimate which provides a certain tradeoff between specificity and sensitivity, or operating point.


It would be convenient if the sampling rate could vary from word to word, unlike conventional sampling where the sampling rate is fixed across the vocabulary.

in our experiments, we will impose a floor to make sure that the sample contains at least 20 documents for each term.


Therefore, we expectâ M LE has smaller MSE thanâ M F .

the proposed MLE method is more accurate than the MF baseline, in terms of variance, bias and mean square error.


Recall does fall, but precision does not rise.

there seems to be a ceiling for precision.


In all, the system uses 11 real-valued parameters, weights and thresholds, and we wanted to learn optimal values for these.

we wanted to be able to target either high recall or high precision, As we noted above, precision was much more difficult, and for a summarization task, much more important.


We exclude these triples from our analysis; however, we do include articles and ¡ ¢ ¡ s with multiple interactions.

we tackle cases such as the example above of the pair AIP1, HIV-1-p6 (that can both bind and incorporate) as long as the evidence for the different interactions is given by two different articles.


The task tackled here is actually slightly more difficult: given some sentences extracted from article £ , assign to £ an interaction type ¤ and extract the proteins ¡ ¢ ¡ involved.

for the purpose of classification, we act as if we do not have information about the proteins that interact.


While work in transfer learning often considers how the old task should affect learning on the new task, in this paper we show that it helps to take into account how the new task affects the old.

we perform joint decoding of separately-trained sequence models, preserving uncertainty between the tasks and allowing information from the new task to affect predictions on the old task.


Although this maximization cannot be done in closed form, it can be optimized numerically.

effective are gradientbased methods that use approximate second-order information, such as conjugate gradient and limited-memory BFGS (Byrd et al., 1994).


To perform joint decoding at test time, we form the composition of the individual CRFs, viewed as finitestate transducers.

we define a new linear-chain CRF whose state space is the cross product of the states of the individual CRFs, and whose transition costs are the sum of the transition costs of the individual CRFs.


Previous work on the seminars data has used a onefield-per-document evaluation.

for each field, the CRF selects a single field value from its Viterbi path, and this extraction is counted as correct if it exactly matches any of the true field mentions in the document.


On the most challenging fields, location and speaker, cascaded transfer is more accurate than no transfer at all, and joint decoding is more accurate than cascaded decoding.

for speaker, we see an error reduction of 8% by using joint decoding over cascaded.


For instance library A 2 -g3 contains those bi-phrases obtained by combining 1 or 2 cepts from alignment A, and in which neither the source nor the target phrase contains more than 3 gaps.

library B 1 -g0 does not contain any non-contiguous bi-phrases.


Some of our best results were obtained with relatively small libraries (just over 200,000 entries) of short bi-phrases.

it's not how many bi-phrases you have, it's how good they are.


To determine the true class of each word in a generated translation hypothesis, we use the word error rate (WER).

a target word is considered correct if it is aligned to itself in the Levenshtein alignment between hypothesis and reference translation(s).


The word translation model could be improved in a variety of ways, drawing upon the large body of work on word-sense disambiguation.

there are many types of context features which could be used to improve word translation performance, but which are not available to standard machinetranslation systems.


The chunklink script searches for empty nodes in the parse tree to perform some of its operations.

any S node that contains an empty subject NP and a VP is reduced to just a VP node, and then combined with any immediately-preceding VP nodes to create a single VP constituent.


However, it is also likely true that these longer distance constraints will on occasion enable the context-free parser to better identify the shallow constituent structure.

despite having very similar performance, our shallow parser and the Charniak context-free parser are likely making complementary predictions about the shallow structure that can be exploited for further improvements.


Practically, the projection framework is sensitive to component errors.

poor word alignments significantly degrade the accuracy of the projected annotations.


Because it is well known that handling unknown words is a serious problem for POS taggers, our strategy for selecting sentences for manual annotation is to maximize the word coverage of the in ital model.

we wish to find a small set of sentences that would lead to the greatest reduction of currently unknown words Finding these sentences is a NP-hard problem because the 0/1 knapsack problem could be reduced to this problem in polynomial-time (Gurari, 1989).


The parameters of T back are estimated as follows: where α(t) is a discounting coefficient and β is set to satisfy that all words P (w|t) = 1.

the discounting coefficient is computed using the Witten-Bell discounting method: where C anno (t) is the count of tokens whose tag is t in the manually annotated corpus and S anno (t) is the seen types of words with tag t. we trust the parameter estimates from the model trained on manual annotation by default unless it is based on unreliable statistics.


In an additional experiment with Igbo, we found that post-processing can improve performance substantially even when an OCR system trained on Igbo characters is the starting point.

the commercial OCR system used for Igbo experiments supports user-trained character shape models.


Related words probably should translate as related words in the other language as well.

we are trying to capture word-level semantic information.


It is worth noting that for all the dictionaries we generate, the total number of entries is the same, since all the words that occur in the source portion of the corpus have an entry.

precision and recall are proportional to each other and to our evaluation metric.


In this experiment, we took the training set in SENSEVAL-3 as labeled data, and the test set as unlabeled data.

all of dimensionality reduction methods and classifiers can use the label information in training set, but can not access the label information in test set.


Understanding such stories is challenging for a number of reasons.

complex stories contain many sub-events (e.g.


2 Every text segment that contributes to an answer to any of the 30 questions is annotated as an answer.

answer annotations include segments that constitute a partial answer.


Unfortunately, longer answers could present problems for some traditional QA systems.

some of the more sophisticated algorithms that perform additional processing steps such as logical verifiers (Moldovan et al., 2002) may be less accurate or computationally infeasible for longer answers.


In Sections 6 and 7, we propose initial steps towards modifying the traditional QA architecture for use in MPQA.

we propose and evaluate two types of opinion filters for MPQA: subjectivity filters and opinion source filters.


We implement the IR subsystem of a traditional QA system, and apply the subjectivity filters to the IR results.

for each opinion question in the corpus 6 , we do the following: 1.


We test each of the MANUAL, RULEBASED, and NAIVE BAYES subjectivity filters.

we compare the rank of the first answer to each question in the 5 the one they label Naive Bayes 1.


This experiment considers correspondences between (1) the classes (i.e., opinion or fact) assigned by the subjectivity filters to the sentences containing answers, and (2) the classes of the questions the answers are responses to (according to the OpQA annotations).

we compute the probabilities (where ans = answer): P(ans is in a C1 sentence | ans is the answer to a C2 question) for all four combinations of C1=opinion, fact and C2=opinion, fact.


Last, w[a] shall be a weight assigned to the arc by the recognizer.

w[a] = p ac (a) 1/λ • P LM (a) with acoustic likelihood p ac (a), language model probability P LM , and languagemodel weight λ.


The frequency of words in written text has been shown to follow Zipf's law.

if the words are ordered in terms of frequency, the relationship between frequency and rank can be approximated with the following equation.


For each character l, substitution distance is equally distributed over all other characters and the deletion operation (l → ).

the initial probability for a character match was set to 90%, and the remaining 10% was equally distributed over the other 26 possible substitutions.


It is no wonder manual feedback shows excellent and stable performance if enough relevant documents are available, hence the challenge is how it keeps high performance with less amount of manual relevance judgment.

we restrict the manual judgment to the minimum amount, namely only a relevant document and a non-relevant document.


Okapi sgt also outperforms except for R05P.


Instead, the window consists of two orthonormal bases, one on either side of an utterance.

a region of utterances above the test utterance is projected, utterance by utterance, into an orthonormal basis, and likewise a region of utterances below the test utterance is projected into another orthonormal basis.


Subsequent works (Tishby et al., 1999;Gedeon et al., 2003) have studied and motivated further the earlier distributional clustering method.

it can be shown that the algorithm of Figure  1 locally minimizes the following cost function: where H denotes entropy 1 and X, Y and C are formal variables whose values range over all data elements, features and clusters, respectively.


However, for the case of non-contiguous segments, we would like to represent higher-order dependencies that are difficult to model in Viterbi.

in Figure 3b we definitely want a feature bridging the gap between Bill and Clinton, which could not be captured with a standard first-order model.


We thank the members of the Penn BioIE project for the development of the CYP450 corpus that we used for our experiments.

seth Kulick answered many questions about the data.


We have empirically found, however, that slightly non-causal and quite localized estimates of the vowel category probability is sufficient to achieve user satisfaction.

we obtain probability estimates of the form , where V is a vowel class, and X t−τ , .


, X t+τ are feature frames within a length 2τ + 1 window of features centered at time t. After several empirical trials, we decided on neural networks for vowel classification because of the availability of efficient discriminative training algorithms and their computational simplicity.

we use a simple 2-layer multi-layer perceptron (Bishop, 1995) whose input layer consists of 26 * 7 = 182 nodes, where 26 is the dimension of X t , the MFCC feature vector, and 2τ + 1 = 7 is the number of consecutive frames, and that has 50 hidden nodes (the numbers 7 and 50 were determined empirically).


To recognize the discrete control signals, HMMs are employed since, as in standard speech recognition, time warping is necessary to normalize for different signal durations corresponding to the same class.

we embed phone HMMs into "word" (C, CV, or CVC) HMMs.


Several additional direct VJ-applications have also been developed.

we have directly interfaced the VJ system into a simple blocks world environment, where more precise object movement is possible than via the mouse driver.


Specifically, we have directly interfaced the VJ system into a simple blocks world environment, where more precise object movement is possible than via the mouse driver.

this environment can draw arbitrary trajectories, and can precisely measure user fidelity when moving an object along a trajectory.


Various factors of the entire system might influence the performance in real dialogue which is evaluated by the log data.

the users might not have answered the questions appropriately, or the replies might not have been correctly recognized.


Using θ to represent the vector of all model parameters, if our data were fully observed, we could express our learning problem as Because of the factorization of the joint distribution, this learning task would be trivial, computable in closed form from relative frequency counts.

unfortunately, in our training set the variables , o and r are hidden (not observed), leaving us with a much harder optimization problem: we want model parameters which maximize the expected likelihood of the observed data, where the expectation is taken over the hidden variables for each instance.


A semantic represention capturing the meaning of a superlative requires a more sophisticated analysis.

it is important to explicitly represent the comparison set of a superlative.


Surprisingly, however, it is rarely mentioned in statistical Machine Translation.

state-of-the-art phrase-based SMT relies on a phrasetable-a large set of ngram pairs over the source and target languages, along with their translation probabilities.


Over the three dialogues, the annotation yielded K = .845 for the old/med/new classification (K = .788 when including the finer-grained subtype distinction).

"old" proved to be the easiest to distinguish, with K = .902; for "med" and "new" agreement was measured at K = .800 and K = .794, respectively.


Second, and most importantly, we believe that using more features will be the main trigger of higher accuracy.

we plan to use additional lexical and relational features derived from knowledge sources such as WordNet (Fellbaum, 1998) and FrameNet (Baker et al., 1998) which should be especially helpful in distinguishing mediated from new entities, the most difficult decision to make.


We want to use pivot features from our unlabeled data to put domain-specific words in correspondence.

Figure 3: SCL Algorithm we want the pivot features to model the fact that in the biomedical domain, the word signal behaves similarly to the words investments, buyouts and jail in the financial news domain.


Since each instance contains features which are totally predictive of the pivot feature (the feature itself), we never use these features when making the binary prediction.

we do not use any feature derived from the right word when solving a right token pivot predictor.


Furthermore, θ is a projection from the original feature space onto R h .

θx is the desired mapping to the (low dimensional) shared feature representation.


MIRA learns and outputs a linear classification score, s(x, y; w) = w • f (x, y), where the feature representation f can contain arbitrary features of the input, including the correspondence features described earlier.

mIRA aims to learn weights so that the score of correct output, y t , for input x t is separated from the highest scoring incorrect outputs 2 , with a margin proportional to their Hamming losses.


Finally, we are investigating more direct ways of applying structural correspondence learning when we have labeled data from both source and target domains.

the labeled data of both domains, not just the unlabeled data, should influence the learned representations.


A number of decision variables are under our control which exert influence on the objective function.

they have to be optimised in order to maximise (or minimise) the objective function.


Secondly, our system suffers from poor next best solutions.

if the best solution violates some constraints, then we find the next best solution is typically worse than the best solution with violated constraints.


The algorithm maximizes the logprobability of the entire NomBank labeled parse tree.

assuming we only have two classes "ARG" and "NONE", the log-probability of a NomBank labeled parse tree is defined by Equation 2.


Arguments are classified from left to right in the textual order they appear.

for arguments that are already labeled, we also add their argument class r. for each argument to the left of the current argument, we have a feature b3-b4-b5-b6r.


The second model is defined as the product of the probabilities of selecting all lexical entries in the sentence and the root node probability of the parse tree.

the second model is also defined without the probabilities on phrase structures: where Z model2 is the sum over the set of all possible parse trees for the sentence.


Another possible reason for the accuracy is the constraints placed by unification-based grammars.

incorrect parse trees were suppressed by the constraints.


By combining such fragments of information, it is possible to incrementally arrive at a consolidated lexical entry for that word.

the precision grammar itself drives the incremental learning process within a parsing context.


An alternate approach is to compile out a set of word templates for each lexical type (with the important qualification that they do not rely on preprocessing of any form), and check for corpus occurrences of an unknown word in such contexts.

the morphological, syntactic and/or semantic predictions implicit in each lexical type are made explicit in the form of templates which represent distinguishing lexical contexts of that lexical type.


In the case of the ERG, this consists of a finite state machine which feeds into lexical rules; in the case of JACY, segmentation and lemmatisation is based on a combination of ChaSen (Matsumoto et al., 2003) and lexical rules.

we are able to assume that the Japanese data has been pre-segmented in a form compatible with JACY, as we are able to replicate the automatic pre-processing that it uses.


Execution time for our own re-implementation of Palmer (2005)  This paper has shown that basic syntactic information is useful for Semantic role labeling using instance-based learning techniques.

the following have been demonstrated: 1.


Execution time for the instance-based learning presented here is about four times faster for SRL than the comparable approach used by Palmer, (2005).

pARA plays an important role reducing the overhead during classification when using instance-based learning.


The main advantage of the proposed approach is that it is mainly data-driven and requires only minimally annotated corpus for training whilst retaining the understanding robustness and deepness for spoken language.

the two classifiers are trained using weakly supervised strategies: the former one is trained through the combination of active learning and self-training (Tur et al., 2005), and the latter one is trained using a practical bootstrapping technique.


The results indicate that bootstrapping methods can effectively make use of the unlabeled data to improve the semantic classification performance.

the learning curve of bootstrapping 1 achieves more significant improvement than the curve of bootstrapping 2.


This is to ensure that our prosody analysis is fair and does not catch any cues from the laughs.

we make sure that our speaker turns are clean and not garbled by laughs.


The table also shows that all features except internal silence and tempo show significant differences across two genders, although only pitch features (Max-F0, Min-F0, and StdDev-F0) show the effect of interaction between gender and humor.

the effect of humor on these pitch features is dependent on gender.


Our prosody analysis revealed that humorous and non-humorous turns indeed show significant differences in most of these features, even when accounted for the speaker and gender differences.

we found that humorous turns tend to have higher tempo, smaller internal silence, and higher peak, range and standard deviation for pitch and energy, compared to non-humorous turns.


Building hooks is like applying special unary rules on top of nonhooks.

in terms of of outside heuristic for hooks, there is a slight difference from that for non-hooks: we do not need the backward-looking estimate for the left boundary word u.


Next, each NP in an English tree is aligned to an NP or PP in the corresponding German tree in a way that is consistent with the word-alignment information.

the words dominated by the English node must be aligned only to words dominated by the German node, and vice versa.


We also assume a vectorᾱ ∈ R d of parameter values.

we define the score for any partial or complete decision sequence y given the definitions above, the output structure F (x) for an input x is the highestscoring well-formed structure for x: To decode with the model we use a beam-search method.


Transliteration candidates that include many characters derived from the impression model are preferred.

the Kanji characters derived via the impression model are used to rerank the candidates derived via the pronunciation model.


The characteristics of transliteration differ according to the languages involved.

the exact transliteration of say, an English name is highly dependent on the language since this will be influenced by the difference in the phonological systems of the language pairs.


Having investigated the effect of the training size, Sporleder & Lapata (2006) came to the conclusion that their system performs well being trained on a small data set.

the learning curve for German shows an improvement of only about 2% when the amount of training data is increased from 20%, which in case of German fiction approximately equals 370,000 tokens, to 100%.


Actually, this construction process by combination is executed in the pattern matching step.

we do not off-line construct all possible combination patterns from the components and only construct the combination patterns that are able to match the target.


We measured the accuracy of the IE task using the same criterion as Bunescu and Mooney (2006), who used an SVM to construct extraction patterns on word/POS/type sequences from the AImed corpus.

an extracted interaction from an abstract is correct if the proteins are tagged as interacting with each other somewhere in that abstract (document-level measure).


k¥ are combined like two pieces of a jigsaw puzzle where the only constraint is that two pieces may not overlap.

we append all (rotational and translational) variants of r to any free site adjacent to the site of l's last monomer, and add all resulting viable conformations c (ie.


Therefore, CKY identifies the substring or chart energy landscape of each sequence, a function f ¡ i j¢ which maps substrings ¡ i j¢ to their lowest accessible energy level.

since the energy of a conformation in the HP model is determined by the number of HH contacts, f unlike standard energy functions, f has no local minima.


This methodology allows us to test a range of predictions that CCG makes about priming.

we present evidence for priming between lexical and syntactic categories encoding partially satisfied subcategorization frames, and we show that priming effects exist both for incremental and normal-form CCG derivations.


In the present paper, we build on this corpus-based approach to priming, but focus on the role of the underlying syntactic representations.

we use priming to evaluate claims resulting from a particular syntactic theory, which is a way of testing the representational assumptions it makes.


However, these rules' probabilities are now collectively determined by a smaller set of shared parameters.

4 because the distribution of the child features β and γ no longer depends arbitrarily on the rest of the rule.


Determining whether or not a speaker supports a proposal falls within the realm of sentiment analysis, an extremely active research area devoted to the computational treatment of subjective or opinion-oriented language (early work includes Wiebe and Rapaport (1988), Hearst (1992), Sack (1994), and Wiebe (1994); see Esuli (2006) for an active bibliography).

since we treat each individual speech within a debate as a single "document", we are considering a version of document-level sentiment-polarity classification, namely, automatically distinguishing between positive and negative documents (Das and Chen, 2001;Pang et al., 2002;Turney, 2002;Dave et al., 2003).


The #("support") − #("oppos") baseline is meant to explore whether the speech-segment classification task can be reduced to simple lexical checks.

this method uses the signed difference between the number of words containing the stem "support" and the number of words containing the stem "oppos" (returning the majority class if the difference is 0).


However, it is interesting to consider whether we really need to consider relationships specifically between speech segments themselves, or whether it suffices to simply consider relationships between the speakers of the speech segments.

as an alternative to using same-speaker links, we tried a speaker-based approach wherein the way we determine the initial individual-document classification score for each speech segment uttered by a person p in a given debate is to run an SVM on the concatenation of all of p's speech segments within that debate.


In contrast, our work concerns the summarization of fine-grained information about opinions.

while recent research efforts have shown that fine-grained opinions (e.g.